{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#导入常用的数据分析工具包\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,Dropout,Flatten\n",
    "from keras.layers.convolutional import Conv1D,MaxPooling1D\n",
    "from keras.preprocessing import sequence\n",
    "from keras.layers.embeddings import Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入影评数据集\n",
    "from keras.datasets import imdb\n",
    "(X_train,y_train),(X_test,y_test) = imdb.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.数据理解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集的大小: (25000,)\n",
      "训练标签的大小: (25000,)\n",
      "测试集的大小: (25000,)\n",
      "测试标签的大小: (25000,)\n"
     ]
    }
   ],
   "source": [
    "#查看数据集的规模\n",
    "print('训练集的大小:',X_train.shape)\n",
    "print('训练标签的大小:',y_train.shape)\n",
    "print('测试集的大小:',X_test.shape)\n",
    "print('测试标签的大小:',y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463, 4369, 5012, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5, 647, 4, 116, 9, 35, 8163, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37, 4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 6853, 5, 163, 11, 3215, 10156, 4, 1153, 9, 194, 775, 7, 8255, 11596, 349, 2637, 148, 605, 15358, 8003, 15, 123, 125, 68, 23141, 6853, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 36893, 1157, 15, 299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 8255, 5, 25249, 656, 245, 2350, 5, 4, 9837, 131, 152, 491, 18, 46151, 32, 7464, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95]\n"
     ]
    }
   ],
   "source": [
    "print(X_train[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(y_train[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "imdb数据集是对原始数据集处理之后的数据集，每一个文本内容都是一段数字序列。标签0表示消极，1表示积极。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.concatenate((X_train,X_test),axis = 0)\n",
    "y = np.concatenate((y_train,y_test),axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000,)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#查看这50000个文本，每个文本的长度\n",
    "text_length = [len(word) for word in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文本长度的均值: 234.75892\n",
      "文本长度的标准差: 172.91149458735703\n",
      "文本长度的最小值: 7\n",
      "文本长度的最大值: 2494\n"
     ]
    }
   ],
   "source": [
    "print('文本长度的均值:',np.mean(text_length))\n",
    "print('文本长度的标准差:',np.std(text_length))\n",
    "print('文本长度的最小值:',np.min(text_length))\n",
    "print('文本长度的最大值:',np.max(text_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化文本长度的分布\n",
    "text_length = np.array(text_length)\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.subplot(121)\n",
    "plt.boxplot(text_length)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(text_length,bins = 50)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.数据准备\n",
    "\n",
    "- 采用词嵌入模型，将文本中的每一个单词映射到高维向量空间中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#指定向量空间中词语的数量\n",
    "top_words = 200000\n",
    "#指定每个文本的长度\n",
    "max_words = 500\n",
    "#指定经过词嵌变换后的输出向量维度\n",
    "out_dimension = 32\n",
    "\n",
    "#限定样本的长度\n",
    "X_train = sequence.pad_sequences(X_train,maxlen = max_words)\n",
    "X_test = sequence.pad_sequences(X_test,maxlen = max_words)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.构建卷积神经网络对文本进行分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#指定向量空间中词语的数量\n",
    "top_words = 200000\n",
    "#指定每个文本的长度\n",
    "max_words = 500\n",
    "#指定经过词嵌变换后的输出向量维度\n",
    "out_dimension = 32\n",
    "#指定批的大小\n",
    "batch_size = 256\n",
    "#指定训练的轮次\n",
    "epochs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "#添加嵌入层\n",
    "model.add(Embedding(top_words,out_dimension,input_length = max_words))\n",
    "#添加卷积层\n",
    "model.add(Conv1D(filters = 32,kernel_size = 3,padding = 'same',activation = 'relu'))\n",
    "model.add(MaxPooling1D(pool_size = 2))\n",
    "model.add(Flatten())\n",
    "\n",
    "#添加全连接层\n",
    "model.add(Dense(256,activation = 'relu'))\n",
    "model.add(Dense(1,activation = 'sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 500, 32)           6400000   \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 500, 32)           3104      \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 250, 32)           0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8000)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 256)               2048256   \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 8,451,617\n",
      "Trainable params: 8,451,617\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#配置该模型\n",
    "model.compile(loss = 'binary_crossentropy',optimizer = 'rmsprop',metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "74/74 - 17s - loss: 0.6114 - acc: 0.6356 - val_loss: 0.5773 - val_acc: 0.6933\n",
      "Epoch 2/10\n",
      "74/74 - 17s - loss: 0.2803 - acc: 0.8897 - val_loss: 0.3381 - val_acc: 0.8584\n",
      "Epoch 3/10\n",
      "74/74 - 16s - loss: 0.1692 - acc: 0.9339 - val_loss: 0.2838 - val_acc: 0.8885\n",
      "Epoch 4/10\n",
      "74/74 - 17s - loss: 0.1064 - acc: 0.9633 - val_loss: 0.3154 - val_acc: 0.8920\n",
      "Epoch 5/10\n",
      "74/74 - 17s - loss: 0.0712 - acc: 0.9755 - val_loss: 0.3154 - val_acc: 0.8930\n",
      "Epoch 6/10\n",
      "74/74 - 17s - loss: 0.0400 - acc: 0.9875 - val_loss: 0.3594 - val_acc: 0.8893\n",
      "Epoch 7/10\n",
      "74/74 - 17s - loss: 0.0251 - acc: 0.9916 - val_loss: 0.4258 - val_acc: 0.8869\n",
      "Epoch 8/10\n",
      "74/74 - 17s - loss: 0.0125 - acc: 0.9963 - val_loss: 0.4921 - val_acc: 0.8885\n",
      "Epoch 9/10\n",
      "74/74 - 17s - loss: 0.0089 - acc: 0.9977 - val_loss: 0.5576 - val_acc: 0.8854\n",
      "Epoch 10/10\n",
      "74/74 - 17s - loss: 0.0084 - acc: 0.9980 - val_loss: 0.6519 - val_acc: 0.8832\n"
     ]
    }
   ],
   "source": [
    "#训练样本数据\n",
    "history = model.fit(X_train,y_train,batch_size = batch_size,epochs = epochs,validation_split = 0.25,verbose = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.绘制模型的训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The loss changes with training epochs')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.subplot(121)\n",
    "plt.plot(history.history['acc'],'r-',label = 'train accuracy')\n",
    "plt.plot(history.history['val_acc'],'b-',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Epochs');plt.ylabel('Accuracy');plt.title('The accuracy changes with training epochs')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(history.history['loss'],'r-',label = 'train loss')\n",
    "plt.plot(history.history['val_loss'],'b-',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.xlabel('Epochs');plt.ylabel('Loss');plt.title('The loss changes with training epochs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上面的训练结果可以看出，随着训练轮次的增加，训练精度在不断提高，训练损失在不断下降。但是在验证集上的表现，随着训练轮次的增加，验证精度不在提高，验证损失过了最低点之后持续上升，说明该模型发生了过拟合现象。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.使用处理序列数据最主流的LSTM模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import LSTM\n",
    "\n",
    "model_lstm = Sequential()\n",
    "model_lstm.add(Embedding(top_words,out_dimension,input_length = max_words))\n",
    "#添加LSTM层\n",
    "model_lstm.add(LSTM(units = 100))\n",
    "#添加全连接层\n",
    "model_lstm.add(Dense(1,activation = 'sigmoid'))\n",
    "\n",
    "#配置该神经网络\n",
    "model_lstm.compile(loss = 'binary_crossentropy',optimizer = 'rmsprop', metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "74/74 [==============================] - 290s 4s/step - loss: 0.6037 - acc: 0.6970 - val_loss: 0.4627 - val_acc: 0.8133\n",
      "Epoch 2/10\n",
      "74/74 [==============================] - 347s 5s/step - loss: 0.3591 - acc: 0.8530 - val_loss: 0.3438 - val_acc: 0.8622\n",
      "Epoch 3/10\n",
      "74/74 [==============================] - 349s 5s/step - loss: 0.2503 - acc: 0.9069 - val_loss: 0.4239 - val_acc: 0.7946\n",
      "Epoch 4/10\n",
      "74/74 [==============================] - 743s 10s/step - loss: 0.2034 - acc: 0.9262 - val_loss: 0.3150 - val_acc: 0.8682\n",
      "Epoch 5/10\n",
      "74/74 [==============================] - 257s 3s/step - loss: 0.1577 - acc: 0.9424 - val_loss: 0.4309 - val_acc: 0.8818\n",
      "Epoch 6/10\n",
      "74/74 [==============================] - 358s 5s/step - loss: 0.1182 - acc: 0.9600 - val_loss: 0.4249 - val_acc: 0.8430\n",
      "Epoch 7/10\n",
      "74/74 [==============================] - 370s 5s/step - loss: 0.0898 - acc: 0.9694 - val_loss: 0.3469 - val_acc: 0.8822\n",
      "Epoch 8/10\n",
      "74/74 [==============================] - 369s 5s/step - loss: 0.0781 - acc: 0.9737 - val_loss: 0.6205 - val_acc: 0.8355\n",
      "Epoch 9/10\n",
      "74/74 [==============================] - 369s 5s/step - loss: 0.0571 - acc: 0.9817 - val_loss: 0.4246 - val_acc: 0.8552\n",
      "Epoch 10/10\n",
      "74/74 [==============================] - 366s 5s/step - loss: 0.0500 - acc: 0.9844 - val_loss: 0.4045 - val_acc: 0.8723\n"
     ]
    }
   ],
   "source": [
    "history = model_lstm.fit(X_train,y_train,batch_size = batch_size,epochs = epochs,validation_split = 0.25,verbose = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The loss changes with training epochs')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化模型的结果\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.subplot(121)\n",
    "plt.plot(history.history['acc'],'r--',label = 'train accuracy')\n",
    "plt.plot(history.history['val_acc'],'g--',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Epochs');plt.ylabel('Accuracy');plt.title('The accuracy changes with training epochs')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(history.history['loss'],'r--',label = 'train loss')\n",
    "plt.plot(history.history['val_loss'],'g--',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.xlabel('Epochs');plt.ylabel('Loss');plt.title('The loss changes with training epochs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图的训练结果可以看出，模型在训练集上的精度持续提高，在验证集上的精度曲线了较大的波动。模型仍然存在过拟合现象。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.结合CNN和LSTM，并加入Dropout层防止过拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = Sequential()\n",
    "final_model.add(Embedding(top_words,out_dimension,input_length = max_words))\n",
    "#添加全连接层\n",
    "final_model.add(Conv1D(filters = 32,kernel_size = 3,padding = 'same',activation = 'relu'))\n",
    "final_model.add(MaxPooling1D(pool_size = 2))\n",
    "\n",
    "#添加LSTM层\n",
    "final_model.add(LSTM(units = 100))\n",
    "final_model.add(Dense(1,activation = 'sigmoid'))\n",
    "final_model.add(Dropout(0.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#配置该网络\n",
    "final_model.compile(loss = 'binary_crossentropy',optimizer = 'rmsprop',metrics = ['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "74/74 - 152s - loss: 2.7036 - acc: 0.5732 - val_loss: 0.6265 - val_acc: 0.5078\n",
      "Epoch 2/10\n",
      "74/74 - 156s - loss: 2.5506 - acc: 0.7197 - val_loss: 0.4525 - val_acc: 0.8419\n",
      "Epoch 3/10\n",
      "74/74 - 150s - loss: 2.5259 - acc: 0.7650 - val_loss: 0.4518 - val_acc: 0.7998\n",
      "Epoch 4/10\n",
      "74/74 - 155s - loss: 2.5048 - acc: 0.7913 - val_loss: 0.4627 - val_acc: 0.8237\n",
      "Epoch 5/10\n",
      "74/74 - 158s - loss: 2.4846 - acc: 0.7915 - val_loss: 0.4555 - val_acc: 0.8013\n",
      "Epoch 6/10\n",
      "74/74 - 158s - loss: 2.4964 - acc: 0.8094 - val_loss: 0.3273 - val_acc: 0.8808\n",
      "Epoch 7/10\n",
      "74/74 - 159s - loss: 2.4066 - acc: 0.8187 - val_loss: 0.5415 - val_acc: 0.8171\n",
      "Epoch 8/10\n",
      "74/74 - 158s - loss: 2.4020 - acc: 0.8235 - val_loss: 0.3985 - val_acc: 0.8664\n",
      "Epoch 9/10\n",
      "74/74 - 159s - loss: 2.3849 - acc: 0.8293 - val_loss: 0.3927 - val_acc: 0.8638\n",
      "Epoch 10/10\n",
      "74/74 - 159s - loss: 2.4168 - acc: 0.8272 - val_loss: 0.3682 - val_acc: 0.8712\n"
     ]
    }
   ],
   "source": [
    "history = final_model.fit(X_train,y_train,batch_size = batch_size,epochs = epochs,validation_split = 0.25,verbose = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The loss changes with training epochs')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化模型的结果\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.subplot(121)\n",
    "plt.plot(history.history['acc'],'r--',label = 'train accuracy')\n",
    "plt.plot(history.history['val_acc'],'g--',label = 'validation accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Epochs');plt.ylabel('Accuracy');plt.title('The accuracy changes with training epochs')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(history.history['loss'],'r--',label = 'train loss')\n",
    "plt.plot(history.history['val_loss'],'g--',label = 'validation loss')\n",
    "plt.legend()\n",
    "plt.xlabel('Epochs');plt.ylabel('Loss');plt.title('The loss changes with training epochs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上图结果可知，使用CNN+LSTM对文本数据进行分类，效果还是非常的不错。再次建议大家使用callbacks回调函数对最好的模型进行保存。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用训练好的数据对测试集进行预测\n",
    "y_pred = final_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = np.where(y_pred>0.5,1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型的预测精度: 0.84856\n"
     ]
    }
   ],
   "source": [
    "print('模型的预测精度:',sum(y_pred.squeeze() == y_test)/len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.92      0.86     12500\n",
      "           1       0.90      0.78      0.84     12500\n",
      "\n",
      "    accuracy                           0.85     25000\n",
      "   macro avg       0.86      0.85      0.85     25000\n",
      "weighted avg       0.86      0.85      0.85     25000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "综合accuracy,precision,recall,f1-score等指标，我们训练出来的final_model的效果还不错。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
